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Recurrentgemma 9B by alpindale

 »  All LLMs  »  alpindale  »  Recurrentgemma 9B   URL Share it on

  8   
  Arxiv:1705.03551   Arxiv:1804.06876   Arxiv:1804.09301   Arxiv:1809.02789   Arxiv:1811.00937   Arxiv:1904.09728   Arxiv:1905.07830   Arxiv:1905.10044   Arxiv:1907.10641   Arxiv:1911.01547   Arxiv:1911.11641   Arxiv:2009.03300   Arxiv:2009.11462   Arxiv:2101.11718   Arxiv:2103.03874   Arxiv:2107.03374   Arxiv:2108.07732   Arxiv:2109.07958   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2203.09509   Arxiv:2206.04615   Arxiv:2304.06364   Arxiv:2402.19427   Autotrain compatible   Endpoints compatible   Recurrent gemma   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/alpindale/recurrentgemma-9b 

Recurrentgemma 9B Benchmarks

LLME Score: 0.19504
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Recurrentgemma 9B (alpindale/recurrentgemma-9b)

Recurrentgemma 9B Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Research, Commercial applications
Applications:
Text generation, Creative content creation, Chatbots, Text summarization, NLP research, Language Learning Tools.
Primary Use Cases:
Text generation tasks, Question answering, Summarization, Reasoning
Limitations:
Biases or gaps in training data, Complexity of tasks, Language ambiguity, Factual inaccuracies
Considerations:
Users should adhere to responsible usage guidelines and ensure ethical considerations are addressed.
Supported Languages 
English (proficient)
Training Details 
Data Sources:
Same training data and data processing as used by the Gemma model family
Methodology:
Recurrent architecture developed at Google
Context Length:
4096
Hardware Used:
TPUv5e, JAX, ML Pathways
Model Architecture:
Recurrent
Safety Evaluation 
Methodologies:
Structured evaluations, Internal red-teaming testing
Findings:
Acceptable thresholds for meeting internal policies for safety categories.
Risk Categories:
Text-to-text content safety, Representational harms, Memorization, Large-scale harm
Responsible Ai Considerations 
Fairness:
Biases are addressed through careful scrutiny, input data pre-processing, and evaluations reported.
Transparency:
Details on models' architecture, capabilities, limitations, and evaluation processes are summarized.
Mitigation Strategies:
Continuous monitoring using evaluation metrics and potential exploration of de-biasing techniques.
Input Output 
Input Format:
Text string (e.g., a question, a prompt, or a document to be summarized)
Accepted Modalities:
text
Output Format:
Generated English-language text in response to the input
LLM NameRecurrentgemma 9B
Repository 🤗https://huggingface.co/alpindale/recurrentgemma-9b 
Model Size9b
Required VRAM19.3 GB
Updated2025-02-05
Maintaineralpindale
Model Typerecurrent_gemma
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   4.4 GB: 4-of-4
Model ArchitectureRecurrentGemmaForCausalLM
Licensegemma
Transformers Version4.42.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than alpindale/recurrentgemma-9b.

Rank the Recurrentgemma 9B Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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